Deep feature extraction and fine κ-nearest neighbour for enhanced human papillomavirus detection in cervical cancer – a comprehensive analysis of colposcopy images by Lipsarani Jena, Santi Behera, Srikanta Dash, Prabira Sethy

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A big data analysis algorithm for massive sensor medical images by Sarah A. Alzakari, Nuha Alruwais, Shaymaa Sorour, Shouki A. Ebad, Asma Abbas Hassan Elnour, Ahmed Sayed

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Comprehensive Evaluation of Techniques for Intelligent Chatter Detection in Micro-Milling Processes by Guilherme Serpa Sestito, Wesley Angelino De Souza, Alessandro Roger Rodrigues, Maira Martins Da Silva

“…Random Forest, Pearson’s Correlation, and collinearity analysis are the strategies used to identify the most relevant features in these operations. …”
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Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence by Hedayetul Islam, Md. Sadiq Iqbal, Muhammad Minoar Hossain

“…Principal component analysis (PCA) and recursive feature elimination (RFE) algorithms were used as feature optimizers. …”
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HTTD: A Hierarchical Transformer for Accurate Table Detection in Document Images by Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Mahmoud Abdalla, Hyun-Soo Kang

Subjects: “…table detection…”
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Enhancing sarcasm detection in sentiment analysis for cyberspace safety using advanced deep learning techniques by Raghu Dhumpati, Archana Sasi, Shaik Johny Basha, Suryanarayana Vadhri, P. Saroja, Swathi Alluri, Ramesh Vatambeti

Subjects: “…Sarcasm detection…”
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Autism Spectrum disorder Detection in Toddlers and Adults Using Deep Learning by Abbas Sidra, Ojo Stephen, Krichen Moez, Alamro Meznah A., Mihoub Alaeddine, Vilcekova Lucia

“…Data analysis is carried out to detect anomalies and outliers, and a label encoding technique is utilized to convert the categorical data into numerical values. …”
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Intelligent Detection of Oceanic Front in Offshore China Using EEFD-Net with Remote Sensing Data by Ruijie Kong, Ze Liu, Yifei Wu, Yong Fang, Yuan Kong

Subjects: “…automatic detection algorithm…”
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Computer Vision for Substrate Detection in High‐Throughput Biomaterial Screens Using Bright‐Field Microscopy by Robert Owen, Aishah Nasir, Mahetab H. Amer, Chenxue Nie, Xuan Xue, Laurence Burroughs, Chris Denning, Ricky D. Wildman, Faraz A. Khan, Morgan R. Alexander, Felicity R. A. J. Rose

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Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis by Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad Alturise

“…In addition, The SHAP analysis was used to identify the most important features in classification. …”
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Passive Radar Low Slow Small Detection Dataset (LSS-PR-1.0) and Multi-domain Feature Extraction and Analysis Methods by Xiaolong CHEN, Guilin RAO, Jian GUAN, Jinhao WANG, Hongyong WANG, Caisheng ZHANG, Jianxin YI, Xianrong WAN, Yunhua RAO

“…On this basis, four categories of ten multi-domain feature extraction and analysis methods are proposed, including time-domain features (relative average amplitude), frequency-domain features (spectral features, Doppler waterfall plot, and range Doppler features), time-frequency-domain features, and motion features (heading difference, trajectory parameters, speed variation interval, speed variation coefficient, and acceleration). …”
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A Study of Feature Combination for Vehicle Detection Based on Image Processing by Jon Arróspide, Luis Salgado

“…In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. …”
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JDroid: Android malware detection using hybrid opcode feature vector by Recep Sinan Arslan

“…In this study, we propose a tool called JDroid that treats opcodes (Dalvik Opcode and Java ByteCode) as features based on static analysis. The proposed tool aims to detect malicious applications with a unique ensemble model in a stacked generalised structure that uses different opcode sequences as a hybrid, and where each feature is first trained separately and then used by an ensemble decision. …”
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Research and Application of SURF and Gray Difference in Detection of Small Modulus Plastic Gear Defect by Yang Ya, Tao Hongyan, Yu Chengbo

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Acoustic emission detection and defect identification method for micro-leakage in natural gas pipeline valves by Lizhen ZHANG, Wen'ao WANG

“…These intricate and redundant time-frequency domain feature matrices were transformed into two-dimensional and threedimensional feature factor matrices utilizing Principal Component Analysis (PCA). …”
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The Bright Feature Transform for Prominent Point Scatterer Detection and Tone Mapping by Gregory D. Vetaw, Suren Jayasuriya

“…This paper introduces a fast image-processing method to visually identify and detect point scatterers in synthetic aperture imagery using the bright feature transform (BFT). …”
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Detection of Road Rage in Vehicle Drivers Based on Speech Feature Fusion by Xiaofeng Feng, Chenhui Liu, Ying Chen

“…To improve the detection efficiency, principal component analysis (PCA) is used to reduce the dimensionality of the fused features. …”
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Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection by Yanjun Feng, Jun Liu, Yonggang Gai

“…This mapping leverages the unique geometric properties of hyperbolic space, particularly the hyperbolic distance metric, to represent the distances between features more effectively. Next, the most relevant features for anomaly detection are selected through the anomaly-aware feature subset selection module, enhancing anomaly detection performance. …”
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Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection by Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang

“…Visualization of EEG data in low-dimensional feature space can ease the annotation to support subsequent supervised learning for seizure detection. …”
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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…We conducted a comprehensive evaluation of the proposed approach against several well-known machine learning models, including AdaBoostM1 (AbM1), K-nearest neighbor (KNN), J48-Decision Tree (J48), multilayer perceptron (MLP), stochastic gradient descent (SGD), naïve Bayes (NB), and logistic model tree (LMT). The comparative analysis demonstrates the effectiveness and superiority of our method across various performance metrics, highlighting its potential to significantly enhance the capabilities of network intrusion detection systems.…”
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